# TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models

Source: [TomatoDIFF: On-plant Tomato Segmentation with Denoising Diffusion Models](<https://www.sota2.com/research/paper/68fa6fe71e209131cea480bb>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:29:17.533Z

Authors: Marija Ivanovska, Vitomir Struc, Janez Pers

Published: 2023-07-03T14:43:40.000Z

## Abstract

Artificial intelligence applications enable farmers to optimize crop growth and production while reducing costs and environmental impact. Computer vision-based algorithms in particular, are commonly used for fruit segmentation, enabling in-depth analysis of the harvest quality and accurate yield estimation. In this paper, we propose TomatoDIFF, a novel diffusion-based model for semantic segmentation of on-plant tomatoes. When evaluated against other competitive methods, our model demonstrates state-of-the-art (SOTA) performance, even in challenging environments with highly occluded fruits. Additionally, we introduce Tomatopia, a new, large and challenging dataset of greenhouse tomatoes. The dataset comprises high-resolution RGB-D images and pixel-level annotations of the fruits.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2307.01064>)

[PDF](<https://arxiv.org/pdf/2307.01064.pdf>)